Abstract：
BACKGROUND: Diabetic retinopathy (DR) is a serious eye complication that results in permanent vision damage. As the number of patients suffering from DR increases, so does the delay in treatment for DR diagnosis. To bridge this gap, an efficient DR screening system that assists clinicians is required. Although many artificial intelligence (AI) screening systems have been deployed in recent years, accuracy remains a metric that can be improved.
METHODS: An enumerative pre-processing approach is implemented in the deep learning model to attain better accuracies for DR severity grading. The proposed approach is compared with various pre-trained models, and the necessary performance metrics were tabulated. This paper also presents the comparative analysis of various optimization algorithms that are utilized in the deep network model, and the results were outlined.
RESULTS: The experimental results are carried out on the MESSIDOR dataset to assess the performance. The experimental results show that an enumerative pipeline combination K1-K2-K3-DFNN-LOA shows better results when compared with other combinations. When compared with various optimization algorithms and pre-trained models, the proposed model has better performance with maximum accuracy, precision, recall, F1 score, and macro-averaged metric of 97.60%, 94.60%, 98.40%, 94.60%, and 0.97, respectively.
CONCLUSIONS: This study focussed on developing and implementing a DR screening system on color fundus photographs. This artificial intelligence-based system offers the possibility to enhance the efficacy and approachability of DR diagnosis.
METHODS: An enumerative pre-processing approach is implemented in the deep learning model to attain better accuracies for DR severity grading. The proposed approach is compared with various pre-trained models, and the necessary performance metrics were tabulated. This paper also presents the comparative analysis of various optimization algorithms that are utilized in the deep network model, and the results were outlined.
RESULTS: The experimental results are carried out on the MESSIDOR dataset to assess the performance. The experimental results show that an enumerative pipeline combination K1-K2-K3-DFNN-LOA shows better results when compared with other combinations. When compared with various optimization algorithms and pre-trained models, the proposed model has better performance with maximum accuracy, precision, recall, F1 score, and macro-averaged metric of 97.60%, 94.60%, 98.40%, 94.60%, and 0.97, respectively.
CONCLUSIONS: This study focussed on developing and implementing a DR screening system on color fundus photographs. This artificial intelligence-based system offers the possibility to enhance the efficacy and approachability of DR diagnosis.
摘要：
背景：糖尿病性视网膜病变（DR）是一种严重的眼部并发症，可导致永久性视力损害。随着DR患者数量的增加，DR诊断的治疗延迟也是如此。为了弥合这个差距，需要一个有效的DR筛查系统来帮助临床医生。尽管近年来已经部署了许多人工智能（AI）筛查系统，准确性仍然是一个可以改进的指标。
方法：在深度学习模型中实现了枚举式预处理方法，以获得更好的DR严重程度分级精度。将所提出的方法与各种预训练模型进行比较，并列出了必要的性能指标。本文还对深度网络模型中使用的各种优化算法进行了比较分析,并概述了结果。
结果：在MESSIDOR数据集上进行实验结果以评估性能。实验结果表明，与其他组合相比，枚举管道组合K1-K2-K3-DFNN-LOA显示出更好的结果。与各种优化算法和预训练模型相比，所提出的模型具有更好的性能和最大的精度，精度,召回，F1得分,宏观平均指标为97.60%，94.60%,98.40%,94.60%,和0.97。
结论：本研究的重点是开发和实施彩色眼底照片DR筛查系统。这种基于人工智能的系统提供了增强DR诊断的有效性和可接近性的可能性。
方法：在深度学习模型中实现了枚举式预处理方法，以获得更好的DR严重程度分级精度。将所提出的方法与各种预训练模型进行比较，并列出了必要的性能指标。本文还对深度网络模型中使用的各种优化算法进行了比较分析,并概述了结果。
结果：在MESSIDOR数据集上进行实验结果以评估性能。实验结果表明，与其他组合相比，枚举管道组合K1-K2-K3-DFNN-LOA显示出更好的结果。与各种优化算法和预训练模型相比，所提出的模型具有更好的性能和最大的精度，精度,召回，F1得分,宏观平均指标为97.60%，94.60%,98.40%,94.60%,和0.97。
结论：本研究的重点是开发和实施彩色眼底照片DR筛查系统。这种基于人工智能的系统提供了增强DR诊断的有效性和可接近性的可能性。
